Instructions to use Hriday75/qwen2.5-3b-infectious-disease-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Hriday75/qwen2.5-3b-infectious-disease-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-infectious-disease-lora") - Notebooks
- Google Colab
- Kaggle
Qwen2.5-3B Infectious Disease Expert (LoRA)
Model Details
Model Description
This is a highly specialized, locally fine-tuned LoRA adapter for the Qwen2.5-3B model, designed specifically for infectious diseases, virology, and epidemiology. It was trained to act as a domain expert in a Multi-LoRA routing system.
- Developed by: Hriday75
- Model type: LoRA Adapter (PEFT)
- Language: English
- Finetuned from base model:
unsloth/Qwen2.5-3B-bnb-4bit - Training Stage: Phase 1 (Continued Pre-Training on Domain Knowledge)
Uses
This model is intended to be used as a dynamically loaded adapter. When attached to the base Qwen2.5-3B model, it acts as a highly technical infectious disease expert.
Training Details
Training Data
This adapter was trained on high-quality, unstructured medical literature specifically focused on infectious diseases. The primary dataset used was KushT/LitCovid_BioCreative, which contains tens of thousands of parsed PubMed abstracts, virology papers, and epidemiology reports.
Training Procedure
Trained using unsloth for high-efficiency 4-bit LoRA fine-tuning.
How to Get Started with the Model
from peft import PeftModel
from transformers import AutoModelForCausalLM
# 1. Load the base model
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit")
# 2. Attach this Infectious Disease Adapter
model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-infectious-disease-lora")
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